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Joint estimation-segmentation of optic flow

机译:光学流量的联合估计分割

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In this paper we address the intricate issue of jointly recovering the apparent velocity field hetween two consecutive frames and its underlying partition. We design a global cost functional including robust estimators. These estimators enable to deal with the large deviations occurring in the different energy terms and offer the possibility to introduce a simple coupling between a dense optical flow field and a segmentation. This coupling is also reinforced by a parametric likeness term. The resulting estimation-segmentation model thus involves a tight cooperation between a local estimation process and a global modelization. The minimization of the final cost function is conducted efficiently by a multigrid optimization algorithm.
机译:在本文中,我们解决了在两个连续帧和其潜在分区共同恢复表观速度场的复杂问题。我们设计了全球成本职能,包括强大的估算。这些估计器能够使不同能量术语中发生的大偏差能够处理,并且提供了在致密光学流场和分段之间引入简单耦合的可能性。该耦合也由参数相似项加强。因此,产生的估计分割模型涉及本地估计过程与全局建模之间的紧密合作。通过多重资源优化算法有效地进行最终成本函数的最小化。

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